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Achieving a 3.09 percentage-point accuracy gain while reducing model size by 80% demonstrates the power of cross-architecture knowledge distillation in precision agriculture.
Allocating additional computation during decision-making boosts long-horizon robot manipulation success by significantly enhancing subtask prediction accuracy.
